Gap acceptance probability model for pedestrians at unsignalized mid-block crosswalks based on logistic regression

2019 ◽  
Vol 129 ◽  
pp. 76-83 ◽  
Author(s):  
Jing Zhao ◽  
Jairus Odawa Malenje ◽  
Yu Tang ◽  
Yin Han
Keyword(s):  
Logistic Regression ◽  
Probability Model ◽  
Gap Acceptance ◽  
Acceptance Probability

Alternative models and randomization techniques for Bayesian response-adaptive randomization with binary outcomes

2021 ◽  
pp. 174077452110101
Author(s):  
Jennifer Proper ◽  
John Connett ◽  
Thomas Murray
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Error Rate ◽  
Adaptive Design ◽  
Type I Error ◽  
Probability Model ◽  
Binary Outcomes ◽  
Type I ◽  
Operating Characteristics ◽  
Type I Error Rate

Background: Bayesian response-adaptive designs, which data adaptively alter the allocation ratio in favor of the better performing treatment, are often criticized for engendering a non-trivial probability of a subject imbalance in favor of the inferior treatment, inflating type I error rate, and increasing sample size requirements. The implementation of these designs using the Thompson sampling methods has generally assumed a simple beta-binomial probability model in the literature; however, the effect of these choices on the resulting design operating characteristics relative to other reasonable alternatives has not been fully examined. Motivated by the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial, we posit that a logistic probability model coupled with an urn or permuted block randomization method will alleviate some of the practical limitations engendered by the conventional implementation of a two-arm Bayesian response-adaptive design with binary outcomes. In this article, we discuss up to what extent this solution works and when it does not. Methods: A computer simulation study was performed to evaluate the relative merits of a Bayesian response-adaptive design for the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial using the Thompson sampling methods based on a logistic regression probability model coupled with either an urn or permuted block randomization method that limits deviations from the evolving target allocation ratio. The different implementations of the response-adaptive design were evaluated for type I error rate control across various null response rates and power, among other performance metrics. Results: The logistic regression probability model engenders smaller average sample sizes with similar power, better control over type I error rate, and more favorable treatment arm sample size distributions than the conventional beta-binomial probability model, and designs using the alternative randomization methods have a negligible chance of a sample size imbalance in the wrong direction. Conclusion: Pairing the logistic regression probability model with either of the alternative randomization methods results in a much improved response-adaptive design in regard to important operating characteristics, including type I error rate control and the risk of a sample size imbalance in favor of the inferior treatment.

Effects of Salt, Sodium Pyrophosphate, and Sodium Lactate on the Probability of Growth of Escherichia coli O157:H7 in Ground Beef †

2011 ◽  
Vol 74 (4) ◽  
pp. 622-626 ◽  
Author(s):  
CHENG-AN HWANG ◽  
VIJAY JUNEJA
Keyword(s):  
Escherichia Coli ◽  
Logistic Regression ◽  
Probability Model ◽  
Ground Beef ◽  
Sodium Lactate ◽  
Growth Behavior ◽  
Sodium Pyrophosphate ◽  
Escherichia Coli O157 ◽  
E Coli ◽  
Beef Products

Ground beef products are susceptible to contamination with Escherichia coli O157:H7. The objective of this study was to examine the effect of salt, sodium pyrophosphate (SPP), and sodium lactate on the probability of growth of E. coli O157:H7 in ground beef under a temperature abuse condition. Ground beef containing 0 to 2.25% salt, 0 to 0.5% SPP, and 0 to 3% lactate was inoculated with a four-strain mixture of E. coli O157:H7, vacuum packaged, and stored at 10°C for 15 days. A total of 25 combinations of the three additives, each with 20 samples, were tested. A logistic regression was used to model the probability of growth of E. coli O157:H7 (with a 1.0-log CFU/g increase during storage) as a function of salt, SPP, and lactate. The resultant probability model indicated that lactate at higher concentrations decreased the probability of growth of E. coli O157:H7 in ground beef, and the effect was more pronounced at higher salt concentrations. At salt concentrations below 1.3%, the increase of SPP concentration marginally increased the growth probabilities of E. coli O157:H7. The model illustrated the effect of salt, SPP, and lactate on the growth probabilities and growth or no-growth behavior of E. coli O157:H7 in ground beef and can be used to improve the microbial food safety of ground beef products.

scholarly journals MODELING THE PROBABILITY OF SPEEDING BEHAVIOUR AND ACCIDENT INVOLVEMENT USING BINARY LOGISTIC REGRESSION IN EAST JAVA PROVINCE

2020 ◽  
Vol 2 (3) ◽  
pp. 149
Author(s):  
Willy Kriswardhana ◽  
Sonya Sulistyono ◽  
Iin Ervina ◽  
Dadang Supriyanto ◽  
Nunung Nuring Hayati ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Traffic Safety ◽  
High Speed ◽  
Probability Model ◽  
Binary Logistic Regression ◽  
Driving Behavior ◽  
Maximum Speed ◽  
Speed Limit ◽  
Negative Consequences ◽  
Driving Experience

Driving at high speed has negative consequences, namely, the high number of accidents. Several factors have been considered as causes of the increasing severity of victims of traffic accidents, such as a human, vehicle, and environmental factors. The risky driving behavior factor is a factor that needs to be considered in traffic safety studies. This study aims to determine the probability model of speeding behavior based on several driver characteristics and their relationship to accident involvement. This study used a binary logistic regression method to determine the probability of driving behavior exceeding the speed limit and accident involvement. The results showed that the younger a person is, the higher the probability of breaking the maximum speed limit. Furthermore, driving experience also shows a similar trend, where the longer the driving experience of someone, the less likely it is to be involved in an accident. Directions for further research are also presented. Berkendara dengan kecepatan tinggi mempunyai konsekuensi negatif, yaitu tingginya angka kecelakaan. Beberapa faktor telah dipertimbangkan sebagai penyebab dari peningkatan tingkat keparahan korban kecelakaan lalulintas. Faktor tersebut seperti faktor manusia, kendaraan, dan lingkungan. Faktor perilaku berkendara yang berbahaya, menjadi faktor yang perlu diperhatikan dalam kajian keselamatan lalulintas. Penelitian ini bertujuan untuk mengetahui model probabilitas pada perilaku speeding berdasarkan beberapa karakteristik pengendara, serta hubungannya dengan keterlibatan kecelakaan. Penelitian ini menggunakan metode regresi logistik biner untuk mengetahui probabilitas perilaku berkendara melebihi batas kecepatan dan keterlibatan kecelakaan. Hasil penelitian menunjukkan bahwa semakin muda usia seseorang, maka semakin tinggi probabilitasnya dalam melanggar batas kecepatan maksimum. Lebih lanjut diperlihatkan bahwa pengalaman mengemudi juga menunjukkan tren yang serupa. Pengalaman mengemudi seseroang, yang lebih lama akan memperkecil kemungkinan dalam keterlibatan kecelakaan. Arahan untuk penelitian selanjutnya juga ditampilkan.

scholarly journals Perception of landscape, forest and settlement in protected landscape areas

2013 ◽  
Vol 61 (3) ◽  
pp. 721-728
Author(s):  
Jana Mikulec ◽  
Michaela Antoušková
Keyword(s):  
Logistic Regression ◽  
Food Systems ◽  
Life Science ◽  
Probability Model ◽  
Social Conditions ◽  
Institutional Research ◽  
Positive Relation ◽  
Registration Number ◽  
Grant Agency ◽  
Spearman’S Correlation

The paper focuses on perception of landscape, forest and settlement in four Czech protected landscape areas (Kokořínsko, Český Kras, Železné Hory, and Blaník). It studies the relation of perception between the mentioned variables. To study this relation the probability model of logistic regression and Spearman’s correlation coefficient are applied. Necessary data for conducted analysis are collected through visitors’ (both tourists and residents) survey in studied areas. Data collection was effectuated during summer 2011. The results prove the positive relation between studied variables and are supposed to help to improve economical, ecological and social conditions of these areas.Pieces of knowledge introduced in this paper resulted from a solution of the institutional research intention MSM 6046070906 „Economics of resources of Czech agriculture and their efficient use in frame of multifunctional agri-food systems“ and the Internal Grant Agency (IGA) of the Czech University of Life Science in Prague, Registration Number 201111110049.

scholarly journals Statistical Modeling via Bootstrapping and Weighted Techniques Based on Variances

2018 ◽  
Vol 8 (4) ◽  
pp. 3135-3140
Author(s):  
W. M. A. W. Ahmad ◽  
N. A. Aleng ◽  
Z. Ali ◽  
M. S. M. Ibrahim
Keyword(s):  
Logistic Regression ◽  
Probability Model ◽  
Bootstrap Method ◽  
Statistical Technique ◽  
Multiple Logistic Regression ◽  
Statistical Inferences ◽  
Sas Macro ◽  
Real Scenario ◽  
Logistic Regression Algorithm ◽  
Bayesian Logistic Regression

Multiple logistic regression is a methodology of handling dependent variables with a binary outcome. This method is becoming increasingly widespread as a statistical technique that represents a discrete probability model. Many studies have focused on the application but less on the methodology building. This study aims to provide an applied method for multiple logistic regression which is called modified Bayesian logistic regression modeling as an alternative technique for logistic regression analysis that focuses on a combination of the bootstrap method using SAS macro and weighted techniques based on variances using SAS algorithm. Data on oral cancer were applied to illustrate a real scenario of oral health data. This data will be applied to the multiple logistic regression algorithm and modified Bayesian logistic regression. Results from both cases are strongly supported by clinical studies. Through the proposed algorithm, the researcher will have an option whether to analyze the data with the usual or an alternative method. Final results indicate that the modified procedure can provide more efficient results especially for the case which involves statistical inferences.

scholarly journals GAP ACCEPTANCE FOR LEFT TURNS FROM THE MAJOR ROAD AT UNSIGNALIZED INTERSECTIONS

Transport ◽  
2014 ◽  
Vol 32 (3) ◽  
pp. 252-261 ◽  
Author(s):  
Hongmei Zhou ◽  
John N. Ivan ◽  
Per E. Gårder ◽  
Nalini Ravishanker
Keyword(s):  
Information Criterion ◽  
Time Interval ◽  
Major Road ◽  
Gap Acceptance ◽  
Acceptance Probability ◽  
Left Turn ◽  
Unsignalized Intersections ◽  
The Mean ◽  
And Gender ◽  
Gap Models

This paper attempts to identify factors that may influence the gap acceptance behavior of drivers who turn left from the major road at unsignalized intersections. Drivers’ accepted and rejected gaps as well as their age and gender were collected at six unsignalized intersections with both two and four lanes on the major road, with and without the presence of a Left-Turn Lane (LTL), and with both high and low Speed Limits (SLs). Whether or not a driver accepts a given gap was considered as a binary decision and correlated logit models were used to estimate the probability of accepting a gap. Models with different factors were tested and the best model was selected by the quasi-likelihood information criterion. The gap duration, the number of rejected gaps, the mean and total time interval of the rejected gaps and the gender of the driver were all significant in explaining the variation of the gap acceptance probability, whereas the number of lanes of the major road, the presence of LTL, the SL and the driver’s age category were not. Gap acceptance probability functions were determined based on the best model, including both the factors of the number of rejected gaps and the mean time interval of the rejected gaps. As the values of these two factors increase, the probability of accepting a given gap rises up. The developed model can be further applied in practice to improve the analysis of traffic operations and capacity at unsignalized intersections.

scholarly journals Optimizing Pelvic X-Ray indication in blunt trauma patients using clinical criteria

2020 ◽  
Vol 47 ◽  
Author(s):  
JÚLIO PATROCÍNIO MORAES ◽  
JOSÉ GUSTAVO PARREIRA ◽  
PEDRO DE SOUZA LUCARELLI-ANTUNES ◽  
GIOVANNA ZUCCHINI RONDINI ◽  
JACQUELINE ARANTES GIANNINNI PERLINGEIRO ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Blunt Trauma ◽  
Trauma Registry ◽  
Probability Model ◽  
Pelvic Fractures ◽  
Trauma Patients ◽  
Pelvic Examination ◽  
X Ray ◽  
Clinical Criteria

ABSTRACT Objective: to identify a subgroup of blunt trauma patients with very low chance of sustaining pelvic fractures based on clinical criteria. Methods: retrospective analysis of the trauma registry data, collected in a period of 24 months. We selected adult blunt trauma patients who had a PXR on admission. The frequency of pelvic fractures was calculated for the following groups: Normal neurological examination at admission (NNE), hemodynamical stability (HS), normal pelvic examination at admission (NPE), less than 60 years old (ID<60) and absence of distracting injuries (ADI). Logistic regression analysis was carried out in order to create a probability model of negative PXR. Results: an abnormal PXR was identified in 101 (3.3%) out of the 3,055 patients who had undergone a PXR at admission. Out of these, 1,863 sustained a NNE, with 38 positive CXRs (2.0%) in this group. Considering only the 1,535 patients with NNE and HS, we found 28 positive PXRs (1.8%). Out of these, 1,506 have NPE, with 21 abnormal PXRs (1.4%). Of these, 1,202 were younger than 60 y, with 11 positive PXRs (0.9%). By adding all these criteria to the ADI, we found 2 abnormal PXRs in 502 (0.4%) cases. The probability model including all these variables had a 0,89 area under the ROC curve. Conclusions: by adding clinical criteria, it is possible to identify a group of trauma patients with very low chance of sustaining pelvic fractures. The necessity of PXR in these patients needs to be reassessed.

Prediction Model of Severe Coronavirus Disease 2019(COVID-19) Cases Shows the Leading Risk Factor of Hypocalcemia

2020 ◽  
Author(s):  
Chenchan Hu ◽  
Feifei Su ◽  
Jianyi Dai ◽  
Shushu Lu ◽  
Lianpeng Wu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Probability Model ◽  
Multiple Logistic Regression Analysis ◽  
Treatment Programs ◽  
Multiple Logistic Regression ◽  
High Transmission ◽  
Transmission Setting

Abstract Background A striking characteristic of Coronavirus Disease 2019(COVID-19) is the coexistence of clinically mild and severe cases. A comprehensive analysis of multiple risk factors predicting progression to severity is clinically meaningful. Methods The patients were classified into moderate and severe groups. The univariate regression analysis was used to identify their epidemiological and clinical features related to severity, which were used as possible risk factors and were entered into a forward-stepwise multiple logistic regression analysis to develop a multiple factor prediction model for the severe cases.Results 255 patients (mean age, 49.1±SD 14.6) were included, consisting of 184 (72.2%) moderate cases and 71 (27.8%) severe cases. The common symptoms were dry cough (78.0%), sputum (62.7%), and fever (59.2%). The less common symptoms were fatigue (29.4%), diarrhea (25.9%), and dyspnea (20.8%). The univariate regression analysis determined 23 possible risk factors. The multiple logistic regression identified seven risk factors closely related to the severity of COVID-19, including dyspnea, exposure history in Wuhan, CRP (C-reactive protein), aspartate aminotransferase (AST), calcium, lymphocytes, and age. The probability model for predicting the severe COVID-19 was P=1/1+exp (-1.78+1.02×age+1.62×high-transmission-setting-exposure +1.77×dyspnea+1.54×CRP+1.03×lymphocyte+1.03×AST+1.76×calcium). Dyspnea (OR=5.91) and hypocalcemia (OR=5.79) were the leading risk factors, followed by exposure to a high-transmission setting (OR=5.04), CRP (OR=4.67), AST (OR=2.81), decreased lymphocyte count (OR=2.80), and age (OR=2.78). Conclusions This quantitative prognosis prediction model can provide a theoretical basis for the early formulation of individualized diagnosis and treatment programs and prevention of severe diseases.

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